X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=main.py;h=1a17e51a63741da0fa8c9f2910a319c331667c14;hb=a09ee76c8283b7daf4c914df47f86d1964fc25d4;hp=74e70b2a9780601053e51e0d359d082e7a869e1d;hpb=cb737bdbd2f112826f739e4581fbe6546aeef638;p=mygptrnn.git

diff --git a/main.py b/main.py
index 74e70b2..1a17e51 100755
--- a/main.py
+++ b/main.py
@@ -66,6 +66,16 @@ parser.add_argument("--learning_rate", type=float, default=6e-4)
 
 parser.add_argument("--min_learning_rate", type=float, default=6e-5)
 
+# legacy
+
+parser.add_argument("--legacy_lr_schedule", action="store_true", default=False)
+
+parser.add_argument("--legacy_learning_rate", type=float, default=1e-4)
+
+parser.add_argument("--legacy_min_learning_rate", type=float, default=2e-5)
+
+parser.add_argument("--nb_large_lr_epochs", type=float, default=10)
+
 ########################################
 
 parser.add_argument("--model", type=str, default=None)
@@ -460,10 +470,21 @@ for n in vars(args):
 
 ######################################################################
 
-# from nanoGPT
 
+def get_lr(n_epoch, it):
+    if args.legacy_lr_schedule:
+        # my crude scheduling to compare to previous baseline, added
+        # warmup though
+
+        if it < args.nb_warmup_iter:
+            return args.legacy_learning_rate * it / args.nb_warmup_iter
+        elif it < args.nb_large_lr_epochs:
+            return args.legacy_learning_rate
+        else:
+            return args.legacy_min_learning_rate
+
+    # from nanoGPT
 
-def get_lr(it):
     # 1) linear warmup for warmup_iter steps
     if it < args.nb_warmup_iter:
         return args.learning_rate * it / args.nb_warmup_iter
@@ -848,7 +869,7 @@ for n_epoch in range(nb_epochs_finished, nb_epochs):
         total_loss = loss + (args.rho * inner_loss if args.rho > 0 else 0.0)
 
         it += 1
-        lr = get_lr(it)
+        lr = get_lr(n_epoch, it)
         for param_group in optimizer.param_groups:
             param_group["lr"] = lr